To be considered for the studentship, candidates must:
As a minimum:
- Possess an Upper Second (2.1) UK BEng Honours or MEng degree in mathematics, statistics, computer science, electronic and electrical engineering, meterorology or a related discipline from a recognised academic institution
- Have a general interest in renewable energy technology
- Have basic skills in a scripting language (R, Python, MATLAB, or similar)
- For non-native English language speakers, possess a recognised English language qualification ( e.g. IELTS Academic 6.5 overall with all subbands at 5.5 or PTE Academic 62 overall with minimum compoent scores of 51).
- An MSc degree in a numerate subject and/or relevant work experience
- Have a detailed knowledge of wind and/or solar energy technologies and awareness of their interaction with power systems and energy markets.
- Have advanced skills and portfolio of projects in one or more scripting languages
Forecasts of renewable power generation are required for economic and reliable power system operation. In the very-short-term, forecasts are produced by statistical models of generation patterns, including the spatio-temporal dynamics of multiple wind or solar farms in the same region. When forecasting further ahead, meteorological forecasts are used as inputs to predict the output of wind and solar farms. These forecasts are used by participants in electricity markets and by power system operators on a continuous basis to maintain the balance of electricity supply and demand.
This industrial PhD aims to develop improved forecasting methodologies by exploiting contemporary statistical methods for processing large quantities of explanatory data including numerical weather predictions and the wide range of measurements made a wind and solar farms, many of which are available in close to real-time. It will suit candidates with a background in mathematics, statistics, computer science, meteorology, or other numerate disciplines.
The PhD will be carried out in partnership with Natural Power
, a leading renewable energy consultancy, and The DataLab
, a Scottish Innovation Centre. Natural Power will provide industrial supervision, training and context. The student will be expected to work for extended periods at their offices in Stirling and/or Castle Douglas.
Academic supervision will be provided by Dr David McMillan, Lecturer and Dr Jethro Browell, Research Assistant, within the Wind Energy & Control Group of the Institute for Energy and Environment at the University of Strathclyde.
Dr McMillan's primary research interests are reliability and decision analysis, probabilistic modelling and applied statistics with applications in wind energy, asset management, energy policy and energy security.
Dr Browell's primary research interests are in statistical uncertainty and the role it plays in decision-making, with particular reference to short-term wind and windpower forecasting in electricity market participation and power system operation.
Natural Power will provide industrial supervision, training and context. The student will be expected to work for extended periods at their offices in Stirling and/or Castle Douglas.
How to apply
Candidates interested in being considered for the studentship should email a detailed CV with contact information for two academic referees, and a covering letter highlighting their interest and suitability for the project, to Dr Jethro Browell.
If you wish to discuss any details of the project informally, please contact Dr Jethro Browell by email or tel: + 44 (0)141 444 7297.
Short-listed candidates will be called for interview. Interviews will take place on a rolling basis from now until the end of December.
The project begins in January 2018.